Various systems have been devised heretofore to sample spectrum feature parameters through linear predictive analysis. One known system uses a covariance method. The covariance method is described, for example, in document (1) ("DIGITAL PROCESSING OF SPEED SIGNAL", L. R. LABINER/R. W.SCHAFER, Section 8.1, pp. 398-404). Such a conventional system extracts spectrum feature parameters to minimize the value of the estimation function in (1). EQU E=.sub..vertline.z.vertline.=1 .vertline.A(z)Y(z).vertline..sup.2 (dz/2.pi.j) (1)
In the above formula, Y(z) is the z-frequency area representation of the input signal y(to). 1/A(z) is a transfer unction representing the spectral function of an input signal. (z) is represented by the following formula (1-1): ##EQU1## a (i) is a spectrum feature parameter. In this transfer function, one energy concentration (formant) found in a frequency spectrum is represented by two parameters. p is an analysis order. Transforming the formula (1) into a time area results in the estimation function E.sub.t shown in (2). ##EQU2##
N is the number of input signal samples.
The spectrum feature parameter vector a which minimizes the above formula (2) is obtained by solving the following normal equation (5). ##EQU3##
FIG. 5 is a block diagram showing the configuration of a conventional spectrum feature parameter extracting system. The operation of the conventional system is described with reference to FIG. 5.
First, a buffer circuit 2 stores an input signal y(t) sent from an input terminal 1 for a specified length of time N.
A correlation calculation circuit 4 calculates the autocorrelation of the input signal stored in the buffer circuit 2 according to the equation (8) and outputs an autocorrelation matrix R (equation (6)) and the autocorrelation vector b in the formula (7) above. (The vector symbols .fwdarw. above the vectors a, b etc. and the matrix R are omitted.)
A parameter calculation circuit 6 solves the normal equation (5) shown above using the autocorrelation matrix R and the autocorrelation vector b, calculates the spectrum feature parameter vector a, and outputs the result from an output terminal 7.
The Cholesky decomposition algorithm is used to solve the above normal equation (5). For more information on the Cholesky decomposition, refer to document (2) (Discrete-Time Processing of Speech Signals, J. R. Deller et al., Macmillan Pub 1993).